ATM-Based Crevasse Detection & Extraction workflow


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Version 2.2 - published on 25 Mar 2024

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Workflow for crevasse detection with OIB/ATM laser altimeter data

ATM-Based Crevasse Detection & Extraction tool

This workflow implements the crevasse detection with OIB/ATM Laser Altimeter Data algorithm, in parallel over a range of years and user specified operational parameters.

The procedure follows the analysis used to detect the morphology of sea ice features (e.g. pressure ridges) across the Arctic using OIB data (Petty et al., 2016) but applied to crevasses over specific glaciers in Greenland. Some important differences that need to be noted:

  1. For sea ice we assume a relative flat reference surface (sea level) over the 500 m - 1000 m segments we analyze, whereas for these glaciers, the underlying reference surface can vary considerably. We thus experiment with fitting planes to the elevations (e.g. a quadratic plane) and assess height anomalies relative to this surface.

  2. Crevasse depths can be a lot bigger than pressure ridge heights!

  3. Uncertainty regarding the ability of the off-nadir conical scanning laser to accurately penetrate down to the crevasse base.

The crevasse detection algorithm requires NASA Airborne Topographic Mapper (ATM) laser altimetry data collected by NASA's Operation IceBridge, downloaded from, and, corresponding POS AV smoothed best estimate of trajectory (SBET) files downloaded from


Petty, A. A., M. C. Tsamados, N. T. Kurtz, S. L. Farrell, T. Newman, J. P. Harbeck, D. L. Feltham, and J. A. Richter-Menge (2016), Characterizing Arctic sea ice topography using high-resolution IceBridge data, The Cryosphere, 10(3), 1161–1179, doi:10.5194/tc-10-1161-2016.

Studinger, M. 2013, updated 2020. IceBridge ATM L1B Elevation and Return Strength, Version 2. [Indicate subset used]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: [Date Accessed].

Cite this work

Researchers should cite this work as follows:

  • Renette Jones-Ivey; Jeanette Sperhac; Kristin Poinar (2024), "ATM-Based Crevasse Detection & Extraction workflow,"

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